Add figure 1 of T2 chapter

3 T2 Mapping/1 Monoexponential T2 Mapping/01-Introduction copy.md

@@ -0,0 +1,19 @@
+---
+title: Introduction
+subtitle: T2 Mapping
+date: 2024-07-25
+authors:
+  - name:  Samuelle Stonge
+    affiliations:
+      - NeuroPoly Lab, Polytechnique Montreal, Quebec, Canada
+numbering:
+  heading_2: false
+  figure:
+    template: Fig. %s
+---
+
+T2 relaxation, also known as the transverse or spin-spin relaxation, is characterized by the dephasing of spins, leading to a reduction of the total magnetization in the x-y plane. In practical terms, the T2 value represents the upper limit of the signal decay time under ideal imaging conditions. In practice, magnetic field inhomogeneities cause the transverse magnetization to decay faster than what is captured by T2 relaxation. These inhomogeneities can be macroscopic, caused by factors such as metallic implants or air-tissue interfaces, or microscopic, resulting from differences in magnetic susceptibility between tissues (Chavhan et al., 2009; Cohen-Adad, 2014). When considering this phenomenon, we refer to the transverse relaxation as T2*. 
+
+T2 mapping, a quantitative magnetic resonance imaging method, offers images of T2 relaxation times (called T2 maps) providing valuable insights into tissue composition. Given that T2 relaxation is sensitive to specific microstructural changes associated with diseases, such as iron accumulation, myelination and inflammation (Dortch, 2020), it is considered a promising modality for clinical and research applications. Commonly used T2-mapping techniques are split into two categories: the mono-exponential methods, which consider that the voxel’s signal arises from a single tissue compartment, and multi-exponential methods, where various tissues that contribute to a single voxel’s signal are considered. Recently, novel techniques for T2 mapping have emerged beyond the conventional techniques that were developed for NMR, such as MR fingerprinting (Ma et al., 2013), a fast relaxation mapping technique that uses a single image acquisition to quantify multiple parameters (Hamilton et al., 2017). 
+
+In the following sections, we will introduce the current state-of-the-art signal modeling and data fitting theory for both mono-exponential and multi-exponential T2 mapping, as well as the benefits and pitfalls of both approaches. An overview of some common applications of T2 mapping (e.g., myelin water fraction (MWF) imaging) will also be presented. We will also cover the fundamentals of T2* mapping and explore how its signal decay curve can differ from that of T2 relaxation. 

3 T2 Mapping/1 Monoexponential T2 Mapping/02-T2 mapping vs T2-weighted imaging.md

@@ -0,0 +1,22 @@
+---
+title: T2 mapping vs T2-weighted imaging
+subtitle: T2 Mapping
+date: 2024-07-25
+authors:
+  - name:  Samuelle Stonge
+    affiliations:
+      - NeuroPoly Lab, Polytechnique Montreal, Quebec, Canada
+numbering:
+  heading_2: false
+  figure:
+    template: Fig. %s
+---
+
+The standard practice in clinical MRI is T2-weighted imaging (T2w), i.e. images in which the different tissues produce signals according to their T2 relaxation times. T2w images are considered qualitative, as they rely on differences in T2 relaxation of various tissues to provide contrast rather than actual quantitative tissue measurements, thus making their interpretation dependent on the observer. Meanwhile, T2 mapping is considered a quantitative technique, as it directly measures the T2 relaxation time of tissues by fitting multiple data points to a decay curve to calculate the T2 constant. The objective nature of T2 mapping makes it a promising technique for various applications in both clinical practice and research, including disease progression monitoring and the identification of novel biomarkers. Another advantage of T2 mapping is that it allows for consistent comparisons across different imaging protocols, thereby enhancing reproducibility and making it ideal for longitudinal and multi-institutional studies (Margaret Cheng et al., 2012). Figure 1 shows several examples of T2w images acquired at different echo times, compared to the T2 map generated by fitting the decay curve. 
+
+
+:::{figure} #fig3p1cell
+:label: t2Plot1
+T2-weighted images at different TE, compared to T2 map following data fitting of the T2 decay curve. 
+:::
+

binder/data_requirement.json

@@ -1,4 +1,10 @@
-{ "src": "https://drive.google.com/uc?id=1t9M_LfFV74s9QNUxk2qxvFI5b6ZGbxLY",
-  "dst": "../../data",
-  "projectName": "qmrlab-t1-book"
+{
+    "qmrlab-t1-book":{ "src": "https://drive.google.com/uc?id=1t9M_LfFV74s9QNUxk2qxvFI5b6ZGbxLY",
+    "dst": "../data",
+    "projectName": "qmrlab-t1-book"
+    },
+    "03-T2":{ "src": "https://github.com/qMRLab/mooc-data/releases/download/v0.1/03-T2.zip",
+    "dst": "../data",
+    "projectName": "03-T2"
+    }
 }

binder/requirements.txt

@@ -1,5 +1,5 @@
 numpy
 plotly
 networkx
-repo2data
+repo2data==2.9.1
 chart_studio